The present invention relates to techniques for collecting, organizing, and analyzing information from hierarchical multidimensional data sets, and in particular, to systems and methods for improved consumption models for analytics of such data sets.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
In business, analysis of past, present, and future performance is a critical task. A multitude of business-data collection, mining, and analysis products exists for the complex task of evaluating a business's performance, planning for future operations, setting targets, and projecting possible scenarios, conditions, and outcomes. Such products usually involve data stores, such as databases, and the combination of hardware and software for accessing, querying, updating, and analyzing various types of business data stored in the data stores.
For example, businesses, and indeed whole industries, exist around providing enterprise resource planning (ERP) applications to businesses of all sizes. Such ERP applications come in a variety of styles and structures, and can include various types and numbers of constituent applications. Each constituent application can be designed to implement a specific type of analysis geared towards assisting one or more aspects of a business organization. As an example, an ERP application can include both the general ledger accounting program as well as a sales and operations planning (S&OP) programs to collect, analyze, plan, and project financial conditions, sales, and operations of a particular business.
While such highly complex and comprehensive applications, such as an ERP application, exist, the manner in which the information contained in such applications is presented to a user varies greatly. To simplify the consumption of such information, various forms of alphanumeric and graphical analytics displayed in a “dashboard” or home screen have been developed. Analytical applications generate complex bar graphs, pie charts, bullet charts, line graphs, histograms, and many other types of graphic and hybrid alphanumeric-graphic visual representations of the data to quickly and concisely convey the analytic information to a user. While each type of visual representation has its associated advantages and strengths for showing various aspects of the data to aid analysis, comparing disparate types of visual representations of the data is often difficult, if not meaningless.
For instance, pie charts are good at depicting portions of a whole, bar graphs are useful for showing summary information for time periods or categories, and line graphs are appropriate for showing trend data, however, comparing a line graph with a pie graph cannot typically yield any useful information. To address the incongruities in the type of information portrayed by the various types of visual analytics and alpha-numeric analytics, many solutions have been developed to provide users with tools to dynamically interact with analytics to further explore, alter, and enhance the way in which the data is displayed. The intended purpose of such tools relies on chance or trial and error that a user will, in real time, determine the best way to display the information in meaningful ways. Clearly such tools are unstructured and do not provide a consistent framework with which to consume visual analytics.
Thus, there is a need for improved systems and methods for consumption models for analytics. The present invention solves these and other problems by providing systems and methods for structured and scalable visual lattice interface with a predetermined analytic or drill down path.